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Background: Around 15% to 20% of patients with clinically probable Alzheimer disease have been found to have no significant Alzheimer pathology on amyloid positron emission tomography. A previous study showed that conversion to dementia from amyloid-negative mild cognitive impairment (MCI) was observed in up to 11% of patients, drawing attention to this condition.Object: We gathered the detailed neuropsychological and neuroimaging data of this population to elucidate factors for conversion to dementia from amyloid-negative amnestic MCI.Methods: This study was a single-institutional, retrospective cohort study of amyloid-negative MCI patients over age 50 with at least 36 months of follow-up. All subjects underwent detailed neuropsychological testing, 3 tesla brain magnetic resonance imaging), and fluorine-18(18F)-florbetaben amyloid positron emission tomography scans.Results: During the follow-up period, 39 of 107 (36.4%) patients converted to dementia from amnestic MCI. The converter group had more severe impairment in all visual memory tasks. The volumetric analysis revealed that the converter group had significantly reduced total hippocampal volume on the right side, gray matter volume in the right lateral temporal, lingual gyri, and occipital pole.Conclusion: Our study showed that reduced gray matter volume related to visual memory processing may predict clinical progression in this amyloid-negative MCI population. [ABSTRACT FROM AUTHOR]

Amyloid (Aβ) and tau proteins are pathologic hallmarks of Alzheimer disease (AD). It is well known that there is spatial disparity between Aβ and tau protein deposition but, crossed hemispheric accumulation of these 2 proteins has not been reported. Here we report the case of a 76-year-old woman with typical AD who underwent amyloid positron emission tomography (PET) ([ 18 F]-florbetaben) and tau PET scans ([ 18 F]PI-2620), revealing crossed accumulation of Aβ and tau in the cerebral hemisphere. A neuropsychological assessment showed impairment in memory with spared activities of daily living. In the PET analysis, amyloid deposition was observed only in the left side of the cerebral hemisphere and tau only in the right side. Neuroimaging follow-up indicated that the spatial pattern of these protein accumulations had not changed. This case suggests the possibility of independent Aβ and tau pathogenic pathways in AD. [ABSTRACT FROM AUTHOR]

Objective: We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia., Methods: Using OpenPose, a deep learning-based human pose estimation program, we analyzed video clips for resting tremor and finger tapping of the bilateral upper limbs of 55 patients with PD (110 arms). Key motion parameters, including resting tremor amplitude and finger tapping speed, amplitude, and fatigue, were extracted to develop a machine learning-based automatic Unified Parkinson's Disease Rating Scale (UPDRS) rating using support vector machine (SVM) method. To evaluate the performance of this model, we calculated weighted κ and intraclass correlation coefficients (ICCs) between the model and the gold standard rating by a movement disorder specialist who is trained and certified by the Movement Disorder Society for UPDRS rating. These values were compared to weighted κ and ICC between a nontrained human rater and the gold standard rating., Results: For resting tremors, the SVM model showed a very good to excellent reliability range with the gold standard rating (κ 0.791; ICC 0.927), with both values higher than that of nontrained human rater (κ 0.662; ICC 0.861). For finger tapping, the SVM model showed a very good reliability range with the gold standard rating (κ 0.700 and ICC 0.793), which was comparable to that for nontrained human raters (κ 0.627; ICC 0.797)., Conclusion: Machine learning-based algorithms that automatically rate PD cardinal symptoms are feasible, with more accurate results than nontrained human ratings., Classification of Evidence: This study provides Class II evidence that machine learning-based automated rating of resting tremor and bradykinesia in people with PD has very good reliability compared to a rating by a movement disorder specialist., (© 2021 American Academy of Neurology.)